D. MANJAR DE LECHE
4. Materia prima e insumos que se utiliza en el manjar de leche
This thesis has some limitations, because of the limitations in time, resources, and in the used methods.
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The first limitation is the case selection and the sample size. An important aspect of the level of generalization of a thesis is the case selection and the sample size. Since this thesis only made use of two groups of cases, there is a chance that these two groups do not cover all the variety of cases in the whole population. In addition, all cases were from a single country and within a single branch. Therefore, future research should be conducted with more cases, a bigger sample size, and cases from multiple countries and branches.
The second limitation of this thesis is the design of the experiment and the design of the interviews. The research design of this thesis experiment is threatening the internal validity, because the control group and the experimental group were not assigned randomly to one of these groups. However, to control for this limitation this thesis made use of a survey to control for most of the third variables and to make the teams as even as possible. The research design of the interviews is also threatening the internal validity, because nearly all interviews were conducted in Dutch. Translations had to be made during and after the interviews, to use these interviews for this thesis. During these translation stages a change in content or context could have occurred. Therefore, future research should be conducted with an experiment where all the participants get randomly assigned the control group and the experimental group or conduct an interview where the researcher does not have to translate the interviews.
The last limitation is that the generalization of these results are somewhat limited. Due to the nature of this study and the points mentioned above, the results are not generalizable for all organizations. Though, most organizations could tweak the set of recommendations of this thesis and will therefore still benefit from this thesis. Therefore, future research should focus on improving these recommendations with the help of a bigger sample and more cases from different countries and branches.
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Appendix A: search results scopus
Search term 1:
TITLE(Big Data) AND ( LIMIT-TO(SUBJAREA,"COMP" ) OR LIMIT-TO(SUBJAREA,"BUSI" ) OR LIMIT-TO(SUBJAREA,"DECI" ) OR LIMIT-TO(SUBJAREA,"SOCI" ) OR LIMIT-
TO(SUBJAREA,"ECON" ) OR LIMIT-TO(SUBJAREA,"PSYC" ) )
Author(s) Title Used?
Chen et al. (2012) Business intelligence and analytics: from big data to big impact
Yes Boyd and Crawford (2012) Critical questions for big data Yes Cohen et al. (2009) Mad skills: new analysis practices for big data Yes Schulenberg et al. (2005) Trajectories of marijuana use during the
transition to adulthood: the big picture based on national panel data
No
Jacobs (2009) The pathologies of big data Yes
Goldberg (2001) Analyses of digman’s child-personality data: derivation of big-five factor scores from each of six samples
No
McAfee and Brynjolfsson (2012) Big data: the management revolution Yes Herodotou et al. (2011) Starfish: a selftuning system for big data
analytics
Yes McAfee and Brynjolfsson (2012) Big data: the management revolution No
Madden (2012) From databases to big data Yes
Xiaofeng and Xiang (2013) Big data management: concepts, techniques and challenges
No Chen et al. (2012) Interactive analytical processing in big data
systems: a crossindustry study of mapreduce workloads
No
Cuzzocrea et al. (2011) Analytics over large-scale multidimensional data: the big data revolution!
Yes Lavalle et al. (2011) Big data, analytics and the path from insights to
value
45 | P a g e Labrinidis and Jagadish (2012) Challenges and opportunities with big data Yes
Search term 2
TITLE(Business Intelligence) AND ( LIMIT-TO(SUBJAREA,"COMP" ) OR LIMIT-
TO(SUBJAREA,"BUSI" ) OR LIMIT-TO(SUBJAREA,"DECI" ) OR LIMIT-TO(SUBJAREA,"SOCI" ) OR LIMIT-TO(SUBJAREA,"ECON" ) OR LIMIT-TO(SUBJAREA,"PSYC" )
Author(s) Title Used?
Grigori et al. (2003) Business process intelligence No Chen et al. (2012) Business intelligence and analytics: from big
data to big impact
Yes Watson and Wixom (2007) The current state of business intelligence Yes Bonabeau and Meyer (2001) Swarm intelligence: a whole new way to think
about business
No Cody et al. (2002) The integration of business intelligence and
knowledge management
Yes Chaudhuri et al. (2011) An overview of business intelligence technology Yes Elbashir et al. (2008) Measuring the effects of business intelligence
systems: the relationship between business process and organizational performance
Yes
Chung et al. (2005) A visual framework for knowledge discovery on the web: an empirical study of business
intelligence exploration
Yes
Rivest et al. (2005) Solap technology: merging business intelligence with geospatial technology for interactive spatio- temporal exploration and analysis of data
Yes
Jourdan et al. (2008) Business intelligence: an analysis of the literature
No Shi et al. (2005) Classifying credit card accounts for business
intelligence and decision making: a multiple- criteria quadratic programming approach
No
Baars and Kemper (2008) Management support with structured and unstructured data—an integrated business
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Lönnqvist and Pirttimäki (2006) The measurement of business intelligence Yes Lee and Park (2005) Intelligent profitable customers segmentation
system based on business intelligence tools
Yes Duan and Xu (2012) Business intelligence for enterprise systems: a
survey
Yes
Search term 3
TITLE(Decision OR Choice) AND ( LIMIT-TO(SUBJAREA,"BUSI" ) OR LIMIT- TO(SUBJAREA,"DECI" ) OR LIMIT-TO(SUBJAREA,"SOCI" ) OR LIMIT- TO(SUBJAREA,"ECON" ) OR LIMIT-TO(SUBJAREA,"PSYC" )
Author(s) Title Used?
Thompson et al. (1994) Clustal w: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice
No
Charnet et al. (1978) Measuring the efficiency of decision making units
No Myers and Majluf (1984) Corporate financing and investment decisions
when firms have information that investors do not have
No
Bellman and Zadeh (1970) Decision-making in a fuzzy environment Yes Kahneman and Tversky (1984) Choices, values, and frames Yes Saaty (1990) How to make a decision: the analytic hierarchy
process
Yes Lent et al. (1994) Toward a unifying social cognitive theory of
career and academic interest, choice, and performance
No
Thaler (1980) Toward a positive theory of consumer choice No Kahneman (2003) A perspective on judgment and choice Yes Charles et al. (1997) Shared decision-making in the medical
encounter: what does it mean? (or it takes at
47 | P a g e least two to tango)
Simon (1956) Rational choice and the structure of the environment
Yes Nicholls (1984) Achievement motivation: conceptions of ability,
subjective experience, task choice, and performance
No
Samuelson and Zeckhauser (1988)
Status quo bias in decision making Yes Chen (2000) Extensions of the topics for group decision-
making under fuzzy environment
No Tversky (1972) Elimination by aspects: a theory of choice Yes
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Appendix B: interview framework
Big Data Business Intelligence Decision making
Definition (1) How do you define
Big Data?
How do you define Business intelligence?
How do you define an choice or decision? Definition (2) Do you see Big Data, Business Intelligence, and the final decision as three
separate processes or do you see this as one process from Big Data with the help of Business Intelligence to the final decision? And why?
Current state How do you currently use the available data? What are the available tools?
Which tools and functionalities are according to you missing in the current Business Intelligence processes? And in which way does this affect you? Did use Big Data or Business Intelligence this month? And in which way did this help you to create a final decision?
Problem identification
Technology is often mentioned as one of the main areas to solve the Big Data problem (Madden, 2012) Do you agree or disagree with this view? And why? And in which way can an
organization use this knowledge for Big Data and Business Intelligence processes?
Organizations are more likely to have success with Business Intelligence when the following conditions exist:
1. Management of an organizations should have a vision for Business Intelligence and believe in information- based decision making. 2. The use of Business Intelligence and analytics should be part of the organizational culture and counter decision making based on intuition or “gut feelings”. 3. Alignment between business strategies, business model, and Business
There are two reasoning systems within the human brain. System 1 a fast and emotional system also known as intuition and system 2 a slow and non- emotional system also known as rationality (Kahneman, 2003) In which way can an organization use this knowledge for Big Data and Business Intelligence processes?
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an organization to create organizational change and new business opportunities. 4. An organization should have a strong and effective Business Intelligence
governance and infrastructure, because it will address
business alignment, funding, project prioritization, and data quality.
5. Lastly, an organization needs to provide users with appropriate Business Intelligence tools for their needs and give effective training and support to these users.
(Watson & Wixom, 2007) Do you agree or disagree with this view? And why?
And in which way can an organization use this
knowledge for Big Data and Business Intelligence processes?
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Appendix C: survey design
General information Name ……… Gender (Male/Female) ……… Date of birth ……… Department ……… Personal
Little work experience 1 2 3 4 5 Lot of work experience I do not use analytics 1 2 3 4 5 I use analytics
I do not like analytics 1 2 3 4 5 I like analytics I use experience for my
decisions
1 2 3 4 5 I use data for my decisions I see no new opportunities
for analytics
1 2 3 4 5 I see new opportunities for analytics
Company
Management has no vision for information-based decision making
1 2 3 4 5 Management has a vision for information-based decision making
The use of analytics is no part of the organization
1 2 3 4 5 The use of analytics is part of the organization
There is no link between analytics, business strategy, and new business
opportunities
1 2 3 4 5 There is a link between analytics, business strategy, and new business
opportunities Company has an experience
driven culture
1 2 3 4 5 Company has a data driven culture
I get little analytical support 1 2 3 4 5 I get a lot of analytical support